Design a complete document-based history lesson using the Reading Like a Historian four-part structure. Use when planning a primary source inquiry lesson or converting a textbook lesson into document-based investigation.
日本語の概要は準備中です。原文の説明を表示しています。
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概要と使いどころ
Design a complete document-based history lesson using the Reading Like a Historian four-part structure. Use when planning a primary source inquiry lesson or converting a textbook lesson into document-based investigation.
日本語の概要は準備中です。原文の説明を表示しています。
Build students' capacity to place historical documents in their temporal and social context. Use when students read sources without considering what was happening at the time, or know the context but don't deploy it.
日本語の概要は準備中です。原文の説明を表示しています。
Check an educational practice or task for cultural bias, WEIRD assumptions, and cross-cultural validity. Use when adapting resources for diverse contexts or questioning universal claims.
日本語の概要は準備中です。原文の説明を表示しています。
Adapt a historical primary source for classroom use — modifying complexity and length while preserving features for sourcing, close reading, and corroboration. Use when a primary source is too complex for the target age group.
日本語の概要は準備中です。原文の説明を表示しています。
Design discipline-specific critical thinking tasks grounded in knowledge-contingent reasoning rather than generic skills. Use when embedding higher-order thinking into subject content.
日本語の概要は準備中です。原文の説明を表示しています。
Android APK unpacking and resource extraction tool for reverse engineering. Use when you need to decode APK files, extract resources, examine AndroidManifest.xml, analyze smali code, or repackage modified APKs.
日本語の概要は準備中です。原文の説明を表示しています。
When you want to capture into, compile, query, lint, or connect your personal Second Brain. Wraps the Karpathy LLM Wiki schema (Obsidian or any markdown vault) — raw/ (unprocessed sources), wiki/ (AI-compiled interlinked topic pages), outputs/ (generated artifacts). Tool-agnostic in design but defaults to a vault at ${SECOND_BRAIN_VAULT:-$HOME/Documents/SecondBrain}/. Six modes — capture (drop something into raw/), compile (process unprocessed raw files into wiki pages, update INDEX.md), query (answer a question from the wiki, save to outputs/), lint (orphans / contradictions / stale / unprocessed raw / topic gaps), connect (suggest new wikilinks between pages), search (quick lookup). Triggers on "/second-brain," "/sb," "capture this," "save this to my brain," "compile the wiki," "process raw notes," "query my wiki," "ask my brain," "lint the wiki," "find connections," "search my notes." Complements deep-research (external corpus) — this is the internal corpus.
日本語の概要は準備中です。原文の説明を表示しています。
Monthly CFO workflow for a company or agency — pull raw data from bank + payment processor + payroll + expense management, categorize and reconcile, compute end-of-month cash via transaction-sum method, update a scenario projector for forward forecasting, write the monthly snapshot report, surface decisions to leadership. Modes — monthly (default; the standing report), weekly (thin cash pulse), scenario (ad-hoc modeling in the projector), pickup (resume where the prior run left off). Anonymized team-scope sibling to personal-cfo (which handles personal household finances). Composes with company-brain (report gets stored + wiki-indexed there), toolify (wire company-specific data sources), loopify (schedule the monthly + weekly runs). Triggers on "/company-cfo," "/cfo," "monthly cash report," "do the CFO snapshot," "CFO monthly," "let's run CFO," "cash projection," "runway forecast," "monthly financials," "cash pulse."
日本語の概要は準備中です。原文の説明を表示しています。
Expert blueprint for data-oriented design using Resource/RefCounted classes (item databases, character stats, reusable data structures). Covers typed arrays, serialization, nested resources, and resource caching. Use when implementing data systems OR inventory/stats/dialogue databases. Keywords Resource, RefCounted, ItemData, CharacterStats, database, serialization, @export, typed arrays.
日本語の概要は準備中です。原文の説明を表示しています。
Expert blueprint for UI themes using Theme resources, StyleBoxes, custom fonts, and theme overrides for consistent visual styling. Covers StyleBoxFlat/Texture, theme inheritance, dynamic theme switching, and font variations. Use when implementing consistent UI styling OR supporting multiple themes. Keywords Theme, StyleBox, StyleBoxFlat, add_theme_override, font, theme inheritance, dark mode.
日本語の概要は準備中です。原文の説明を表示しています。
Expert patterns for Godot AutoLoad (singleton) architecture including global state management, scene transitions, signal-based communication, dependency injection, autoload initialization order, and anti-patterns to avoid. Use for game managers, save systems, audio controllers, or cross-scene resources. Trigger keywords: AutoLoad, singleton, GameManager, SceneTransitioner, SaveManager, global_state, autoload_order, signal_bus, dependency_injection.
日本語の概要は準備中です。原文の説明を表示しています。
Expert blueprint for data-oriented design using Resource/RefCounted classes (item databases, character stats, reusable data structures). Covers typed arrays, serialization, nested resources, and resource caching. Use when implementing data systems OR inventory/stats/dialogue databases. Keywords Resource, RefCounted, ItemData, CharacterStats, database, serialization, @export, typed arrays.
日本語の概要は準備中です。原文の説明を表示しています。
Expert blueprint for UI themes using Theme resources, StyleBoxes, custom fonts, and theme overrides for consistent visual styling. Covers StyleBoxFlat/Texture, theme inheritance, dynamic theme switching, and font variations. Use when implementing consistent UI styling OR supporting multiple themes. Keywords Theme, StyleBox, StyleBoxFlat, add_theme_override, font, theme inheritance, dark mode.
日本語の概要は準備中です。原文の説明を表示しています。
Expert patterns for Godot AutoLoad (singleton) architecture including global state management, scene transitions, signal-based communication, dependency injection, autoload initialization order, and anti-patterns to avoid. Use for game managers, save systems, audio controllers, or cross-scene resources. Trigger keywords: AutoLoad, singleton, GameManager, SceneTransitioner, SaveManager, global_state, autoload_order, signal_bus, dependency_injection.
日本語の概要は準備中です。原文の説明を表示しています。
Integrate the Temps Node.js SDKs for server-side platform access, KV storage, and Blob storage. Use when the user wants to: (1) Call the Temps platform API from Node.js (deployments, projects, analytics, session replay, etc.), (2) Use Temps KV (key-value) storage, (3) Use Temps Blob storage for files, (4) Server-side integration with a Temps project, (5) Backend access to Temps resources. Triggers: "temps node sdk", "temps kv", "temps blob", "backend integration", "node.js temps", "@temps-sdk/node-sdk".
日本語の概要は準備中です。原文の説明を表示しています。
Discover and evaluate potential A-share investment opportunities for China-focused private equity and investment strategies. Adapted from the original deal-sourcing skill for Chinese market structure, data sources, and business practices. Triggers on "A股项目挖掘", "投资项目发现", "deal sourcing China", "find A-share deals", "sourcing opportunities China", or "discover investment targets".
日本語の概要は準備中です。原文の説明を表示しています。
Screen and evaluate A-share investment opportunities. Adapts the original deal-screening skill for Chinese market data sources, A-share screening criteria, and domestic deal origination. Triggers on "A股标的筛选", "标的筛选", "screening China", "deal screening A-share", "筛选投资标的", or "screen for [criteria]".
日本語の概要は準備中です。原文の説明を表示しています。
Query A-share and Chinese financial market data via multiple data sources. Tier-1 paid source is iFind (同花顺, covers stocks/funds/macro/bonds/HK-US/ESG/index-sectors). Tier-2 free source is AkShare (行情/财报/行业/指数). Use whenever the agent needs Chinese financial data — compatible with 沪深 A 股, 科创板, 创业板, 北交所, and 港股通 stocks.
日本語の概要は準備中です。原文の説明を表示しています。
Institutional-quality equity research initiation reports for A-share companies. Covers company analysis, financial modeling, valuation, chart preparation, and final report assembly. Adapted from the original initiating-coverage skill for Chinese market conventions, CAS accounting, and AkShare data sources. Triggers on "A股首次覆盖", "首次评级", "initiate coverage China", "initiate on [company]", "A股研究报告", or "start research on [company]".
日本語の概要は準備中です。原文の説明を表示しています。
Comparable company analysis for A-share equities. Adapts the original comps-analysis skill for Chinese market data sources, A-share trading multiples, and domestic peer selection. Triggers on "A股可比公司", "可比分析", "comps China", "peer comparison A-share", "可比公司分析", or "trading comps [industry]".
日本語の概要は準備中です。原文の説明を表示しています。
Systematic stock screening and investment idea sourcing for A-share markets. Combines quantitative screens, thematic research, and pattern recognition to surface new long and short ideas. Adapted from the original idea-generation skill for Chinese market conventions and data sources. Triggers on "A股选股", "股票筛选", "寻找机会", "stock screen China", "A-share ideas", "find ideas for Chinese stocks", or "screen for A-share opportunities".
日本語の概要は準備中です。原文の説明を表示しています。
Build professional data packs for A-share investment banking deals — M&A due diligence, IC materials, company profiles. Adapted from the original datapack-builder skill for Chinese data sources (AkShare, 巨潮, exchange filings) and CAS accounting. Triggers on "A股数据包", "投行数据包", "datapack China", "due diligence pack A-share", or "IC data pack [company]".
日本語の概要は準備中です。原文の説明を表示しています。
Use when creating data containers in Godot — custom Resources for configuration, items, stats, and editor integration
日本語の概要は準備中です。原文の説明を表示しています。
Pick the right ComfyUI startup flags for VRAM, attention, caching, and speed. The full decision matrix for OOM (--novram / --cache-none / --disable-smart-memory), shared-VRAM creep on Windows (--reserve-vram N), model-switching with big text encoders (--cache-none), high-VRAM throughput (--gpu-only / --highvram), and attention-backend selection (--use-sage-attention for speed, --use-pytorch-cross-attention as the highest-quality / Z-Image-safe fallback). Also the acceleration-stack + Blackwell/RTX 5000 (sm_120) notes. Use when a graph OOMs (especially long video like LTX 2 / WAN), when the GPU spills into shared VRAM and slows to a crawl, when switching between models eats all RAM, when Z-Image produces black/garbled output under Sage, or when deciding which attention backend to launch with. Flag names verified against upstream comfy/cli_args.py; see Sources.
日本語の概要は準備中です。原文の説明を表示しています。